Fuzzy logic includes 0 and 1 as extreme cases of truth (or “the state of matters” or “fact”) but also includes the various states of truth in between so that, for example, the result of a comparison between two things could be not “tall” or “short” but “. 38 of tallness.”

Fuzzy logic has been used in numerous applications such as facial pattern recognition, air conditioners, washing machines, vacuum cleaners, antiskid braking systems, transmission systems, control of subway systems and unmanned helicopters, knowledge-based systems for multiobjective optimization of power systems.

Current applications Fuzzy logic is an important concept when it comes to medical decision making. Since medical and healthcare data can be subjective or fuzzy, applications in this domain have a great potential to benefit a lot by using fuzzy logic based approaches.

One legacy artificial and machine learning technology is fuzzy logic. Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth — truth values between “completely true” and “completely false.

Fuzzy Synonyms – WordHippo Thesaurus….What is another word for fuzzy?

Lotfi Zadeh

Dr. Lotfi A. Zadeh

1 : marked by or giving a suggestion of fuzz a fuzzy covering of felt a fuzzy stuffed toy. 2 : lacking in clarity or definition moving the camera causes fuzzy photos The line between our areas of responsibility is fuzzy. His reasoning is a little fuzzy.

Development

1. Step 1 − Define linguistic variables and terms. Linguistic variables are input and output variables in the form of simple words or sentences.
2. Step 2 − Construct membership functions for them.
3. Step3 − Construct knowledge base rules.
4. Step 4 − Obtain fuzzy value.
5. Step 5 − Perform defuzzification.

Advantages of Fuzzy Logic in Artificial Intelligence It is a robust system where no precise inputs are required. These systems are able to accommodate several types of inputs including vague, distorted or imprecise data. In case the feedback sensor stops working, you can reprogram it according to the situation.

The construction of Fuzzy Logic Systems is easy and understandable. Fuzzy logic comes with mathematical concepts of set theory and the reasoning of that is quite simple. It provides a very efficient solution to complex problems in all fields of life as it resembles human reasoning and decision making.

The principal components of an FLC system is a fuzzifier, a fuzzy rule base, a fuzzy knowledge base, an inference engine, and a defuzz. ifier. It also includes parameters for normalization. When the output from the defuzzifier is not a control action for a plant, then the system is a fuzzy logic decision system.

A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively …

Fuzzy logic control is based on the fact that an experienced human operator can control a process without knowledge of its dynamics (King and Mamdani, 1977). Developing FLC is usually easier and cheaper than PID controller and FLCs are more robust in that they can cover a wider operation range.

Fuzzy set and crisp set are the part of the distinct set theories, where the fuzzy set implements infinite-valued logic while crisp set employs bi-valued logic. While in crisp sets the transition for an element in the universe between membership and non-membership in a given set is sudden and well defined.

The distinction between fuzzy logic and Boolean logic is that fuzzy logic is based on possibility theory, while Boolean logic is based on probability theory. The advantage of fuzzy logic is that it allows for representing the continuous nature of the soil’s both geographic distribution and attribute distinctness.

Fuzzy set theory has been shown to be a useful tool to describe situations in which the data are imprecise or vague. Fuzzy sets handle such situations by attributing a degree to which a certain object belongs to a set.

Classical set is a collection of distinct objects. For example, a set of students passing grades. Each individual entity in a set is called a member or an element of the set. The classical set is defined in such a way that the universe of discourse is spitted into two groups members and non-members.

A fuzzy set operation is an operation on fuzzy sets. These operations are generalization of crisp set operations. There is more than one possible generalization. The most widely used operations are called standard fuzzy set operations.

Classical sets are sets with crisp boundaries. Usually an ordinary set (a classical or crisp set) is called a collection of objects which have some properties distinguishing them from other objects which do not possess these properties.

Mamdani Fuzzy Inference System

• Step 1 − Set of fuzzy rules need to be determined in this step.
• Step 2 − In this step, by using input membership function, the input would be made fuzzy.
• Step 3 − Now establish the rule strength by combining the fuzzified inputs according to fuzzy rules.

Because of its multidisciplinary nature, the fuzzy inference system is known by numerous other names, such as fuzzy-rule-based system, fuzzy expert system, fuzzy model, fuzzy associative memory, fuzzy logic controller, and simply (and ambiguously) fuzzy system.